# The population doctrine in cognitive neuroscience

arXiv: https://arxiv.org/abs/2104.00145 · HTML: https://mlsanity.com/paper/2104.00145/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2021.

## Built on by (most cited first, 7 of 7)

- [OpenApePose: a database of annotated ape photographs for pose estimation](https://mlsanity.com/paper/2212.00741/) (2022)
- [Identifying Interpretable Visual Features in Artificial and Biological Neural Systems](https://mlsanity.com/paper/2310.11431/) (2023)
- [Choosing the right basis for interpretability: Psychophysical comparison between neuron-based and dictionary-based representations](https://mlsanity.com/paper/2411.03993/) (2024)
- [Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifolds](https://mlsanity.com/paper/2606.25234/) (2026)
- [Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes](https://mlsanity.com/paper/2109.12434/) (2021)
- [Inferring Inference](https://mlsanity.com/paper/2310.03186/) (2023)
- [Self-Organized Learning in Oscillatory Neural Networks with Memristive Signed Couplings](https://mlsanity.com/paper/2607.00286/) (2026)

## Abstract (arXiv)

A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field. Population-level ideas have so far had their greatest impact in motor neuroscience, but they hold great promise for resolving open questions in cognition as well. Here, we codify the population doctrine and survey recent work that leverages this view to specifically probe cognition. Our discussion is organized around five core concepts that provide a foundation for population-level thinking: (1) state spaces, (2) manifolds, (3) coding dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the progress and promise that population neurophysiology holds for cognitive neuroscience-for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing.